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Title: Robust Prediction of Stock Indices using PSO based Adaptive Linear Combiner
Authors: Majhi, R
Panda, G
Majhi, B
Keywords: Adaptive linear combiner;
Error vector;
Long-term prediction;
New model;
Standard PSO;
Stock indices;
Test data;
Training data;
Training sets
Issue Date: 2009
Publisher: IEEE
Citation: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009; Coimbatore; 9 December 2009 through 11 December 2009; Category number CFP0995H; Code 79534; Article number 5393728, Pages 312-317
Abstract: The present paper employs a particle swarm optimization (PSO) based adaptive linear combiner for efficient prediction of various stock indices in presence of strong outliers in the training data. The connecting weights of the model are updated by minimizing the Wilcoxon norm of the error vector by PSO. The short and long term prediction performance of the new model is evaluated with test data and the results obtained are compared with those obtained from the conventional PSO based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and more robust against outliers in training set compared to those obtained by standard PSO based model.
Appears in Collections:Conference Papers

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